基于染色显微镜图像和深度学习,检测船舶压载水中的活微藻
Ming Xie1, Zhichen Liu2, Yu Liu3
1Dalian Maritim University, Navigation College, 1 Linghai Road, Dalian 116026, China.
Marine pollution bulletin
|February 2, 2025
概括
这项研究开发了一种深度学习模型,用于快速检测船舶压载水中的活微藻细胞. 该模型使用中性红色染色和图像处理准确识别微藻,实现活细胞和总细胞的高检测率.
科学领域:
- 海洋生物学 海洋生物学
- 生物技术是生物技术.
- 图像处理 图像处理
背景情况:
- 由于引入非本土物种,包括微藻类,船舶压载水对环境构成风险.
- 快速准确地检测压载水中的微藻类对于有效的管理和环境保护至关重要.
研究的目的:
- 开发和验证用于快速检测和确定微藻细胞活动的深度学习模型.
- 利用中性红色染色和图像处理来区分活的微藻细胞.
主要方法:
- 用中性红色染料染色的微藻的显微镜成像.
- 开发和应用基于深度学习的检测模型.
- 图像处理算法用于分析染色的微观图像.
主要成果:
- 深度学习模型实现了一般微藻检测的高平均精度 (APs) (Platymonas helgolandica tsingtaoensis的99.3%,Alexandrium catenella的98.3%).
- 对活生生的微藻细胞的检测准确度略低,但仍然很高 (91.7%对活生生的P. helgolandica tsingtaoensis,91.9%对活生生的A. catenella).
- 该模型成功确定了微藻细胞的活动.
结论:
- 深度学习模型对于快速检测船舶压载水中的微藻非常有效.
- 中性红色染色与图像处理和深度学习相结合,提供了一种可行的方法来区分活的微藻细胞.
- 开发的模型显示了对压载水的实时监测和管理的承诺,以防止入侵性微藻类物种的传播.
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